相关论文: Detection of Crowdsourcing Cryptocurrency Launderi…
Fraudulent activities on digital banking services are becoming more intricate by the day, challenging existing defenses. While older rule driven methods struggle to keep pace, even precision focused algorithms fall short when new scams are…
Custom officials across the world encounter huge volumes of transactions. With increased connectivity and globalization, the customs transactions continue to grow every year. Associated with customs transactions is the customs fraud - the…
Crowdwork often entails tackling cognitively-demanding and time-consuming tasks. Crowdsourcing can be used for complex annotation tasks, from medical imaging to geospatial data, and such data powers sensitive applications, such as health…
Fuzzing is a widely used technique for detecting vulnerabilities in smart contracts, which generates transaction sequences to explore the execution paths of smart contracts. However, existing fuzzers are falling short in detecting…
Blockchain provides the unique and accountable channel for financial forensics by mining its open and immutable transaction data. A recent surge has been witnessed by training machine learning models with cryptocurrency transaction data for…
Financial fraud increasingly exploits institutional boundaries: laundering networks distribute transactions across multiple banks because no single institution can observe the full pattern. Federated Learning (FL) enables collaborative…
The Bitcoin protocol prevents the occurrence of double-spending (DS), i.e. the utilization of the same currency unit more than once. At the same time a DS attack, where more conflicting transactions are generated, might be performed to…
Multidisciplinary research, in conjunction with artificial intelligence (AI), the Internet of Things (IoT), Blockchain and Big Data analysis, has lowered barriers and made companies more productive, in other words, the joint work of these…
As the size of the datasets getting larger, accurately annotating such datasets is becoming more impractical due to the expensiveness on both time and economy. Therefore, crowd-sourcing has been widely adopted to alleviate the cost of…
Federated learning enables multiple data owners to jointly train a machine learning model without revealing their private datasets. However, a malicious aggregation server might use the model parameters to derive sensitive information about…
Credit cards play an exploding role in modern economies. Its popularity and ubiquity have created a fertile ground for fraud, assisted by the cross boarder reach and instantaneous confirmation. While transactions are growing, the fraud…
Mobile crowdsensing (MCS) is an emerging sensing data collection pattern with scalability, low deployment cost, and distributed characteristics. Traditional MCS systems suffer from privacy concerns and fair reward distribution. Moreover,…
Crowd-intelligence tries to gather, process, infer and ascertain massive useful information by utilizing the intelligence of crowds or distributed computers, which has great potential in Industrial Internet of Things (IIoT). A…
The recent decade witnessed a surge of increase in financial crimes across the public and private sectors, with an average cost of scams of $102m to financial institutions in 2022. Developing a mechanism for battling financial crimes is an…
Criminals have become increasingly experienced in using cryptocurrencies, such as Bitcoin, for money laundering. The use of cryptocurrencies can hide criminal identities and transfer hundreds of millions of dollars of dirty funds through…
Digital crowdsourcing (CS) is a modern approach to perform certain large projects using small contributions of a large crowd. In CS, a taskmaster typically breaks down the project into small batches of tasks and assigns them to so-called…
With market capitalization exceeding USD250 billion by mid-2025, stablecoins have evolved from a crypto-focused innovation into a vital component of the global monetary structure. This paper identifies the characteristics of stablecoins…
Sharding is used to address the performance and scalability issues of the blockchain protocols, which divides the overall transaction processing costs among multiple clusters of nodes. Shards require less storage capacity and communication…
Hyperledger Fabric is a popular permissioned blockchain system that features a highly modular and extensible system for deploying permissioned blockchains which are expected to have a major effect on a wide range of sectors. Unlike…
Crowdsourced machine learning on competition platforms such as Kaggle is a popular and often effective method for generating accurate models. Typically, teams vie for the most accurate model, as measured by overall error on a holdout set,…